Liu Zhi-Ping
Department of Biomedical Engineering, School of Control Science and Engineering, Shandong UniversityJinan, China.
Front Genet. 2017 Jul 13;8:96. doi: 10.3389/fgene.2017.00096. eCollection 2017.
In this work, we provide a comparative study of the main available association measures for characterizing gene regulatory strengths. Detecting the association between genes (as well as RNAs, proteins, and other molecules) is very important to decipher their functional relationship from genomic data in bioinformatics. With the availability of more and more high-throughput datasets, the quantification of meaningful relationships by employing association measures will make great sense of the data. There are various quantitative measures have been proposed for identifying molecular associations. They are depended on different statistical assumptions, for different intentions, as well as with different computational costs in calculating the associations in thousands of genes. Here, we comprehensively summarize these association measures employed and developed for describing gene regulatory relationships. We compare these measures in their consistency and specificity of detecting gene regulations from both simulation and real gene expression profiling data. Obviously, these measures used in genes can be easily extended in other biological molecules or across them.
在这项工作中，我们对用于表征基因调控强度的主要可用关联度量进行了比较研究。在生物信息学中，检测基因（以及RNA、蛋白质和其他分子）之间的关联对于从基因组数据中解读它们的功能关系非常重要。随着越来越多高通量数据集的出现，通过使用关联度量来量化有意义的关系将使数据变得更有意义。已经提出了各种用于识别分子关联的定量度量。它们基于不同的统计假设，用于不同的目的，并且在计算数千个基因的关联时具有不同的计算成本。在这里，我们全面总结了为描述基因调控关系而采用和开发的这些关联度量。我们从模拟和真实基因表达谱数据两方面比较了这些度量在检测基因调控方面的一致性和特异性。显然，这些用于基因的度量可以很容易地扩展到其他生物分子或跨生物分子使用。
